DenseFuse: A Fusion Approach to Infrared and Visible Images

Hui Li; Xiao-jun Wu; Hui Li; Xiao-jun Wu

doi:10.1109/tip.2018.2887342

Abstract

In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, fusion layer and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment.

Affiliated Institutions

Jiangnan University CN

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Publication Info

Year: 2018
Type: article
Volume: 28
Issue: 5
Pages: 2614-2623
Citations: 1589
Access: Closed

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APA Style

                            
                                
                                    Hui Li, 
                                
                                    Xiao-jun Wu, 
                                
                                    Hui Li
                                
                                et al.
                            
                            (2018). 
                            DenseFuse: A Fusion Approach to Infrared and Visible Images. 
                            IEEE Transactions on Image Processing
                            , 28
                            (5)
                            , 2614-2623.
                            https://doi.org/10.1109/tip.2018.2887342
                        

Identifiers

DOI: 10.1109/tip.2018.2887342
PMID: 30575534
arXiv: 1804.08361

Data Quality

Data completeness: 84%